Tuesday, October 17, 2017

This issue is explained by a brief article in the MIT Technology Review, sent by classmate Jorge Cardona. It takes a look at how experts are predicting that blockchain will free data about the grid and reveal more about usage patterns and other critical measures. Here's an excerpt, showing a case of where entrepreneurs are moving in this space:

To unleash the potential of blockchain in the energy sector, Jesse Morris’s team at RMI has joined with Austria-based blockchain startup Grid Singularity to create a new nonprofit called the Energy Web Foundation. Earlier this month, the EWF launched its own blockchain, which Morris says is “purpose-built for the energy sector.” Based on Ethereum, the network will be a test bed for promising use cases. To validate transactions during the test, EWF will rely on 10 major energy companies that have signed on as affiliates.

Classmate Wei Wang sends over the following short article, published in Ars Technica. The piece looks at how attackers have exploited a factorization weakness to steal identities. An excerpt:

A crippling flaw in a widely used code library has fatally undermined the security of millions of encryption keys used in some of the highest-stakes settings, including national identity cards, software- and application-signing, and trusted platform modules protecting government and corporate computers.

The weakness allows attackers to calculate the private portion of any vulnerable key using nothing more than the corresponding public portion. Hackers can then use the private key to impersonate key owners, decrypt sensitive data, sneak malicious code into digitally signed software, and bypass protections that prevent accessing or tampering with stolen PCs. The five-year-old flaw is also troubling because it's located in code that complies with two internationally recognized security certification standards that are binding on many governments, contractors, and companies around the world. The code library was developed by German chipmaker Infineon and has been generating weak keys since 2012 at the latest.

Classmate Nehal Mehta just forwarded on this piece, published by Alexis C. Madrigal in The Atlantic. It looks at how Facebook shaped the outcome of the last election -- and why that effect was so difficult to predict. Key quote:

The information systems that people use to process news have been rerouted through Facebook, and in the process, mostly broken and hidden from view. It wasn’t just liberal bias that kept the media from putting everything together. Much of the hundreds of millions of dollars that was spent during the election cycle came in the form of “dark ads.”

The truth is that while many reporters knew some things that were going on on Facebook, no one knew everything that was going on on Facebook, not even Facebook. And so, during the most significant shift in the technology of politics since the television, the first draft of history is filled with undecipherable whorls and empty pages. Meanwhile, the 2018 midterms loom.

... DeepMind continues to remain independent from its parent company, but its contribution to Google’s product launch is well timed. It reported its first-ever revenues – 40 million pounds ($52 million) in 2016 – from products and services it supplied to other Alphabet companies, according to filings made public on the U.K. business registry Companies House on Monday.

This is just one example of how DeepMind is starting to help Google. Some others that DeepMind has been willing to talk about include supplying algorithms that have helped Google boost the energy efficiency of its data centers by 15 percent, and also improvements to Google’s core ad words product that DeepMind says it cannot detail...

Here's a look at A/B testing, sent over by classmate Rajiv Gupta. The article, published in the Harvard Business Review, looks at the benefits of running experiments on the cheap -- and often. Key quote:

By combining the power of software with the scientific rigor of controlled experiments, your company can create a learning lab. The returns you reap—in cost savings, new revenue, and improved user experience—can be huge. If you want to gain a competitive advantage, your firm should build an experimentation capability and master the science of conducting online tests.

The service began two years ago in India, and Amazon has been slowly marketing it to U.S. merchants in preparation for a national expansion, said the people, who asked not to be identified because the U.S. pilot project is confidential. Amazon is calling the project Seller Flex, one person said. The service began on a trial basis this year in West Coast states with a broader rollout planned in 2018, the people said. Amazon declined to comment.

It's slightly technical, but if someone has any kind of basic programming experience, it can be unbelievably helpful in understanding what's actually going on "under the hood" of bitcoin. It's short but covers everything from hashing to Merkle trees to the actual Blockchain infrastructure. I watched it over the summer and 100% recommend to anyone interested in the topics.

Classmate Varit Sukhum sends along "Managing Our Hub Economy," published in the Harvard Business Review this month. It's a fascinating look at the winner-take-all question which we debated in class, elaborated on in some detail and given a fair trial. Here's a look:

A real opportunity exists for hub firms to truly lead our economy. This will require hubs to fully consider the long-term societal impact of their decisions and to prioritize their ethical responsibilities to the large economic ecosystems that increasingly revolve around them. At the same time, the rest of us—whether in established enterprises or start-ups, in institutions or communities—will need to serve as checks and balances, helping to shape the hub economy by providing critical, informed input and, as needed, pushback.

Classmate Diego Grove sends over this article from the WSJ: "Amazon Takes Over the World." The content should be self-explanatory, but this paragraph, about the MBA experience, may resonate with us.

At New York University’s business school, where I teach, I have for years kept a close watch on which firms are winning the competition for the most talented students. A decade ago, the top recruiter was American Express, with investment banks vying for second position. Now the clear winner is Amazon: 12 students from my most recent class have opted for a life of rain and overrated coffee in the Pacific Northwest.

Tuesday, October 3, 2017

Classmate Sumit Aggarwal sends over an informative article published in the July-August edition of the Harvard Business Review -- "Find the Platform in Your Product". The title is fairly self-explanatory; the article looks at four ways in which products and services can become multisided platforms. Here's a sample:

Why seek to transform products and services into MSPs in the first place? As one Intuit executive told us, it comes down to “fear and greed.” Greed, of course, refers to the potential for new revenue sources that could speed growth and increase a company’s value. Fear refers to the danger that existing and incoming competitors will steal market share from your product or service. Transforming an offering into a platform might enhance your company’s competitive advantage and raise barriers to entry via network effects and higher switching costs. We’re not suggesting that every company should try to emulate Airbnb, Alibaba, Facebook, or Uber. But many companies would benefit from adding elements of a platform business to their offerings.

Classmate Yifei Wu provides a thought-provoking new article from the MIT Technology Review, "Is AI Riding a One-Trick Pony?". The piece is a rejoinder to some of the more optimistic takes on AI that we've seen in the class, as it shows in considerable detail just why the breakthroughs we expect may not be so near. The culprit? Outmoded technology that only appears cutting-edge. Here's an illustrative quote:

When you boil it down, AI today is deep learning, and deep learning is backprop—which is amazing, considering that backprop is more than 30 years old. It’s worth understanding how that happened—how a technique could lie in wait for so long and then cause such an explosion—because once you understand the story of backprop, you’ll start to understand the current moment in AI, and in particular the fact that maybe we’re not actually at the beginning of a revolution. Maybe we’re at the end of one.

Classmate Chin-Chia Liang has sent over an interesting look at health tech platforms, written by Martin Blinder, an entrepreneur in the space. The article examines how technology is altering our awareness of personal health, showing why platforms are driving some of this change. As Blinder writes,

A sleep monitoring app might tell an individual their sleep pattern is irregular, for example, but how do they understand how best to rectify this? Is it because they haven’t been exercising recently? Or perhaps it’s down to how much sugar they’ve consumed. By pulling the data together from all their devices into one place, it’s a lot easier to look at patterns and draw accurate conclusions.

That’s why I believe platforms like ours are cementing their rightful place as the next phase of progression in the health tech space. People want to understand more about what makes them tick, but they need the right tools to make sense of all the lifestyle data they generate every day.

According to the Nikkei, the common identification platform will allow a bank account holder to register for a ‘shared ID’ which could then – as an example – be used to open another account at a different bank. The applicant would be required to provide the shared ID through a smartphone app authenticated through a fingerprint or a facial scan. Fundamentally, the shared ID would negate the need to enter, or re-enter personal information when applying for banking services at a new financial institution.

Classmate Alan Yan has just sent an article from phys.org that answers the question you've always wanted to answer: what's the biggest prime number ever factored? Here are some more details on this thrilling bit of research:

Researchers have set a new record for the quantum factorization of the largest number to date, 56,153, smashing the previous record of 143 that was set in 2012. They have shown that the exact same room-temperature nuclear magnetic resonance (NMR) experiment used to factor 143 can actually factor an entire class of numbers, although this was not known until now. Because this computation, which is based on a minimization algorithm involving 4 qubits, does not require prior knowledge of the answer, it outperforms all implementations of Shor's algorithm to date, which do require prior knowledge of the answer. Expanding on this method, the researchers also theoretically show how the same minimization algorithm can be used to factor even larger numbers, such as 291,311, with only 6 qubits.

Classmate Torsten Walbaum has sent over a couple interesting links that focus on the universal basic income -- what merits it has and how it may be implemented. The first article is from Fortune and it summarizes the problem at hand --

A University of Oxford study from 2013 estimated that 47% of U.S. jobs may be at risk within the next two decades because of advances in artificial intelligence and automation. Last year, the White House Council of Economic Advisers estimated that workers making below $20 an hour would have an 83% chance of losing their jobs to robots in that span. Those odds dropped as workers’ education and income levels grew. But as software gets smarter, that too is subject to change: Companies will eliminate even jobs that were long considered immune from technological displacement.

... and the second piece, published by Futurism.com, is the infographic from which the banner image above it pulled.

Classmate Jorge Cardona has sent along the video below. It features the Last Week Tonight host's entertaining, totally-not-biased take on consolidation.

Along with the video, Jorge asks the following.

On the service part, could this deteriorated service offering end up happening to Amazon, Google, etc.? With Amazon now delivering Chipotle, Shake Shack, etc. how many other industries will they consolidate? With such power consolidation, will regulators step in? If they do, ala Standard Oil, what opportunities could be leveraged?

Here’s how Stitch Fix normally works: Customers join the site, fill out a detailed questionnaire about their size, how they like their clothing to fit, what their style is like, what colors they love and loathe, and how often they dress for certain occasions (like work, events, dates, etc.). Last year, the company introduced a Pinterest integration that lets the company learn more about what customers want. Users can create boards of images they like–which can come from any source–and algorithms analyze the assortment and feed that information into a customer’s profile. Using all that data, an algorithm then mines Stitch Fix’s inventory to find pieces that match their profile.

Classmate Sumika Singh sent over this article, posted on Singularity Hub.

Using the Ethereum blockchain, the UN is attempting to solve some of its most pressing challenges, from refugee resettlement to food distribution. As the article notes:

The UN explains blockchain as “a distributed database that is continuously updated and verified by its users. Each added block of data is ‘chained’ and becomes part of a growing list of records, under the surveillance of network members. This technology enables the transfer of assets and the recording of transactions through a secure database.”

Thursday, September 21, 2017

Classmate Alan Yan provides the following article, "The Meaning of Decentralization," which was just posted on Medium. It's extensive but contains a lot of helpful sections; for example, here's a breakdown of the types of software distribution, included a few paragraphs in.

Architectural (de)centralization — how many physical computers is a system made up of? How many of those computers can it tolerate breaking down at any single time?

Political (de)centralization — how many individuals or organizations ultimately control the computers that the system is made up of?

Logical (de)centralization— does the interface and data structures that the system presents and maintains look more like a single monolithic object, or an amorphous swarm? One simple heuristic is: if you cut the system in half, including both providers and users, will both halves continue to fully operate as independent units?

Classmate Sumit Aggarwal has just sent this interesting look at Finland and how it's using blockchain technology to give refugees a modicum of financial independence. The article, "Finland has a novel way of giving refugees money," is published by the World Economic Forum.

This thesis gives you some idea (but read the full piece to get the entire picture):

The cards mean that the refugees can also receive money – including salaries when they get jobs – and pay bills, without the need to open a bank account.

The blockchain technology used by MONI doesn’t require a financial intermediary, such as a bank, to process transactions. Instead, transactions are instantaneous between users, and a unique digital record is kept of each one. It’s a cheaper payment system that is highly transparent.

If you're still a little confused following our short lecture on blockchain, classmate Sean Lemke has sent along a helpful, brief video from the WSJ -- Bitcoin After Eight Years: More Virtual Than Real?. Sean provides the following additional explanation:

... The interview discusses the SEC’s blocking of Bitcoin being used as the basis of a new ETF offering because of the lack of transparency associated with transactions... The interview also raises some good questions regarding the cryptocurrency’s viability as an investment given its volatility.

Classmate Jerry Woytash has sent over this recommended source for keeping up with all things Bitcoin and blockchain. Matt Levine, columnist for Bloomberg, publishes a running commentary on news related to both -- as well as a host of other issues -- and is worth bookmarking. You can start with his article from last week "Bitcoin Doubts and Buffett Criticisms" and see his aggregated work here.

To supplement Professor Brynjolfsson's previous post, classmate Ahmed Bilal has just sent this quick look at Geoff Hinton, pioneer of AI development, who now expresses hesitations about his brainchild. Here's a key paragraph from the piece,"Artificial intelligence pioneer says we need to start over":

Other scientists at the conference said back-propagation still has a core role in AI's future. But Hinton said that, to push materially ahead, entirely new methods will probably have to be invented. "Max Planck said, 'Science progresses one funeral at a time.' The future depends on some graduate student who is deeply suspicious of everything I have said."

Saturday, September 16, 2017

Geoffrey Hinton has made a number of breakthroughs in neural networks and also had a lot of students and advisees, like Yann LeCun, who made further advances.

Although he's been working in the field for decades, Geoff and his research have gained a lot of well-deserved attention recently as deep learning systems outperformed other approaches on a wide range of tasks, from image recognition to speech.

Many scientists with an amazing contribution like that to their credit would do everything to push it and extend its influence. But Geoff is a modest guy. He now thinks that AI researchers need to move beyond backpropagation and 'start again". The key issue is that, while backpropagation works well on supervised learning problems, where we have a lot of labeled training data, it doesn't work for unsupervised learning, which seems to be how we humans learn most things about the world. When I saw him in Toronto last week, Geoff said it was time for a new wave of researchers with different approaches. He repeated (with a wry smile) the old adage "science advances one funeral at a time."

Fortunately, more and more smart young people are flocking into the field, developing and testing new approaches. The biggest conference in the field, NIPS, keeps getting bigger and selling out faster and faster.

Friday, September 15, 2017

Classmate Subhashree Ringharajan has submitted an article from the MIT Technology Review with this title. Key quote:

As the [AI] technology advances, we might soon cross some threshold beyond which using AI requires a leap of faith. Sure, we humans can’t always truly explain our thought processes either—but we find ways to intuitively trust and gauge people. Will that also be possible with machines that think and make decisions differently from the way a human would? We’ve never before built machines that operate in ways their creators don’t understand. How well can we expect to communicate—and get along with—intelligent machines that could be unpredictable and inscrutable? These questions took me on a journey to the bleeding edge of research on AI algorithms, from Google to Apple and many places in between, including a meeting with one of the great philosophers of our time.

Classmate Jerry Woytash has just sent in an article about Michelangelo, Uber's machine learning platform. There's a lot in here, and the write-up has a lot of technical details, but some key background information is addressed early in the piece:

Michelangelo consists of a mix of open source systems and components built in-house. The primary open sourced components usedare HDFS, Spark, Samza, Cassandra, MLLib, XGBoost, and TensorFlow. We generally prefer to use mature open source options where possible, and will fork, customize, and contribute back as needed, though we sometimes build systems ourselves when open source solutions are not ideal for our use case.

Michelangelo is built on top of Uber’s data and compute infrastructure, providing a data lake that stores all of Uber’s transactional and logged data, Kafka brokers that aggregate logged messages from all Uber’s services, a Samza streaming compute engine, managed Cassandra clusters, and Uber’s in-house service provisioning and deployment tools.

Technologists and health-care professionals across the globe see blockchain technology as a way to streamline the sharing of medical records in a secure way, protect sensitive data from hackers, and give patients more control over their information. But before an industry-wide revolution in medical records is possible, a new technical infrastructure—a custom-built “health-care blockchain”—must be constructed.

Monday, September 11, 2017

Classmate Weixiang Wang has just given you a good reason to fold. As Carnegie Mellon's website details, Libratus, an AI system designed at CMU has just surpassed human competitors at the poker table. Here's a key insight into how the system was able to self-correct and improve:

“After play ended each day, a meta-algorithm analyzed what holes the pros had identified and exploited in Libratus’ strategy,” Sandholm said. “It then prioritized the holes and algorithmically patched the top three using the supercomputer each night. This is very different than how learning has been used in the past in poker. Typically researchers develop algorithms that try to exploit the opponent’s weaknesses. In contrast, here the daily improvement is about algorithmically fixing holes in our own strategy.”

Sandholm also said that Libratus’ end-game strategy, which was computed live with the Bridges computer for each hand, was a major advance.

Classmate Collin Lee has just brought this article to our attention; it speaks to some interesting aspects of the "atoms vs. bits" debate, and the importance of bits ruling atoms in the O2O market. Key quote:

Judging by recent developments, the pure e-commerce model may not be long for the U.S. either. That Amazon and eBay are the only true online shopping sites in the U.S. with any real influence is one piece of evidence. So is the fact that small online retailers are struggling to gain a foothold, while legacy retailers such as Walmart are snapping up online merchants. And yet the most vivid illustration of O2O in action is the escalating turf-war between Amazon and Walmart.

A further short observation, relevant to acquisitions made this summer, rounds out this analysis:

“Just as Walmart is using Bonobos to get access to higher-end consumers and a more technologically savvy way of selling clothes, Amazon is using Whole Foods to get the expertise and physical presence it takes to sell fresh foods,” The New York Times reported.

Thursday, September 7, 2017

If the title confuses or shocks you, that's probably because it's supposed to. Published this morning by Bloomberg, "Mark Sagar Made a Baby in His Lab. Now It Plays the Piano." details the work of Sagar in his lab at the University of Auckland. Sagar's mission is to "humanize AI," to make it more intuitively "alive" and less machine- or robot-like. The applications are many, and more basic than you may think. A relevant section of the piece:

[Humanizing AI] has the potential to yield a more symbiotic relationship between humans and machines. While [Sagar] wasn’t the first to this idea, his approach is unique, a synthesis of his early years as a computer scientist and later ones in the world of Hollywood special effects. The face, he’s concluded, is the key to barreling through the uncanny valley and making virtual beings feel truly lifelike... Soul Machines wants to produce the first wave of likable, believable virtual assistants that work as customer service agents and breathe life into hunks of plastic such as Amazon.com’s Echoand Google Inc.’s Home.

Friday, September 1, 2017

Tom Simonite asks this provocative question in Wired this week. He opens with an anecdote about Microsoft's speech transcription software — how it is now surpassing humans given the same challenges — before expanding into a survey of broader trends:

[The rapid improvement in speech recognition software] is the latest in a string of recent findings that some view as proof that advances in artificial intelligence are accelerating, threatening to upend the economy. Some software has proved itself better than people at recognizing objects such as cars or cats in images, and Google’s AlphaGo software has overpowered multiple Go champions—a feat that until recently was considered a decade or more away.

So, in sum, the rate of technological progress isn't merely continuing at a breakneck speed — it seems to be accelerating beyond it.

Simonite cites a number of AI monitoring programs, including Stanford's One Hundred Year Study on Artificial Intelligence. The article notes that AI monitors are not merely keeping an eye out for how advances in technology may upend the economy. They're also looking at public awareness and perceptions of AI: how many people know these changes are occurring and what do they think of them.

(Also note: the article includes a few graphs showing Moore's Law manifest in a variety of technologies. The improvement in Google's image recognition software is shown below.)